NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins

Michelle S. Scott, Peter V. Troshin, Geoffrey J. Barton

    Research output: Contribution to journalArticle

    62 Citations (Scopus)

    Abstract

    Background: Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting motifs has only recently been carried out.

    Results: Here, we describe NoD, a web server and a command line program that predicts the presence of NoLSs in proteins. Using the web server, users can submit protein sequences through the NoD input form and are provided with a graphical output of the NoLS score as a function of protein position. While the web server is most convenient for making prediction for just a few proteins, the command line version of NoD can return predictions for complete proteomes. NoD is based on our recently described human-trained artificial neural network predictor. Through stringent independent testing of the predictor using available experimentally validated NoLS-containing eukaryotic and viral proteins, the NoD sensitivity and positive predictive value were estimated to be 71% and 79% respectively.

    Conclusions: NoD is the first tool to provide predictions of nucleolar localization sequences in diverse eukaryotes and viruses. NoD can be run interactively online at http://www.compbio.dundee.ac.uk/nod or downloaded to use locally.

    Original languageEnglish
    Article number317
    Pages (from-to)-
    Number of pages7
    JournalBMC Bioinformatics
    Volume12
    DOIs
    Publication statusPublished - 3 Aug 2011

    Keywords

    • nucleolus
    • protein targeting signal
    • protein localization
    • NoD web server
    • NUCLEAR-LOCALIZATION
    • NUCLEOCAPSID PROTEIN
    • VIRUS TYPE-1
    • SIGNAL
    • CONTAINS
    • NUCLEAR/NUCLEOLAR
    • IDENTIFICATION
    • CORONAVIRUS
    • PREDICTION
    • GROWTH

    Cite this

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    title = "NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins",
    abstract = "Background: Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting motifs has only recently been carried out.Results: Here, we describe NoD, a web server and a command line program that predicts the presence of NoLSs in proteins. Using the web server, users can submit protein sequences through the NoD input form and are provided with a graphical output of the NoLS score as a function of protein position. While the web server is most convenient for making prediction for just a few proteins, the command line version of NoD can return predictions for complete proteomes. NoD is based on our recently described human-trained artificial neural network predictor. Through stringent independent testing of the predictor using available experimentally validated NoLS-containing eukaryotic and viral proteins, the NoD sensitivity and positive predictive value were estimated to be 71{\%} and 79{\%} respectively.Conclusions: NoD is the first tool to provide predictions of nucleolar localization sequences in diverse eukaryotes and viruses. NoD can be run interactively online at http://www.compbio.dundee.ac.uk/nod or downloaded to use locally.",
    keywords = "nucleolus, protein targeting signal, protein localization, NoD web server, NUCLEAR-LOCALIZATION, NUCLEOCAPSID PROTEIN, VIRUS TYPE-1, SIGNAL, CONTAINS, NUCLEAR/NUCLEOLAR, IDENTIFICATION, CORONAVIRUS, PREDICTION, GROWTH",
    author = "Scott, {Michelle S.} and Troshin, {Peter V.} and Barton, {Geoffrey J.}",
    year = "2011",
    month = "8",
    day = "3",
    doi = "10.1186/1471-2105-12-317",
    language = "English",
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    journal = "BMC Bioinformatics",
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    }

    NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins. / Scott, Michelle S.; Troshin, Peter V.; Barton, Geoffrey J.

    In: BMC Bioinformatics, Vol. 12, 317, 03.08.2011, p. -.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins

    AU - Scott, Michelle S.

    AU - Troshin, Peter V.

    AU - Barton, Geoffrey J.

    PY - 2011/8/3

    Y1 - 2011/8/3

    N2 - Background: Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting motifs has only recently been carried out.Results: Here, we describe NoD, a web server and a command line program that predicts the presence of NoLSs in proteins. Using the web server, users can submit protein sequences through the NoD input form and are provided with a graphical output of the NoLS score as a function of protein position. While the web server is most convenient for making prediction for just a few proteins, the command line version of NoD can return predictions for complete proteomes. NoD is based on our recently described human-trained artificial neural network predictor. Through stringent independent testing of the predictor using available experimentally validated NoLS-containing eukaryotic and viral proteins, the NoD sensitivity and positive predictive value were estimated to be 71% and 79% respectively.Conclusions: NoD is the first tool to provide predictions of nucleolar localization sequences in diverse eukaryotes and viruses. NoD can be run interactively online at http://www.compbio.dundee.ac.uk/nod or downloaded to use locally.

    AB - Background: Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting motifs has only recently been carried out.Results: Here, we describe NoD, a web server and a command line program that predicts the presence of NoLSs in proteins. Using the web server, users can submit protein sequences through the NoD input form and are provided with a graphical output of the NoLS score as a function of protein position. While the web server is most convenient for making prediction for just a few proteins, the command line version of NoD can return predictions for complete proteomes. NoD is based on our recently described human-trained artificial neural network predictor. Through stringent independent testing of the predictor using available experimentally validated NoLS-containing eukaryotic and viral proteins, the NoD sensitivity and positive predictive value were estimated to be 71% and 79% respectively.Conclusions: NoD is the first tool to provide predictions of nucleolar localization sequences in diverse eukaryotes and viruses. NoD can be run interactively online at http://www.compbio.dundee.ac.uk/nod or downloaded to use locally.

    KW - nucleolus

    KW - protein targeting signal

    KW - protein localization

    KW - NoD web server

    KW - NUCLEAR-LOCALIZATION

    KW - NUCLEOCAPSID PROTEIN

    KW - VIRUS TYPE-1

    KW - SIGNAL

    KW - CONTAINS

    KW - NUCLEAR/NUCLEOLAR

    KW - IDENTIFICATION

    KW - CORONAVIRUS

    KW - PREDICTION

    KW - GROWTH

    U2 - 10.1186/1471-2105-12-317

    DO - 10.1186/1471-2105-12-317

    M3 - Article

    VL - 12

    SP - -

    JO - BMC Bioinformatics

    JF - BMC Bioinformatics

    SN - 1471-2105

    M1 - 317

    ER -